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Change detection using SAR data has been an active topic in various applications. Because conventional change detection identifies signal changes in single-pol radar observations, they cannot separately detect different kinds of change associated with different ground parameters. In this study, we investigated the comprehensive use of dual-pol parameters and proposed a novel dual-pol-based change detection framework utilizing different dual-pol scatter-type indicators. To optimize the exploitation of dual-pol change information, we presented a two-step processing strategy that divides the multiclass change detection process into a binary detection step that identifies the presence of changes and the classification step that distinguishes the types of change. In the detection stage, each dual-pol parameter was considered as an independent information source. Assuming potential conflict between dual-pol parameters, a disjunctive combination of detection results from different dual-pol parameters was applied to obtain the final detection result. In the classification step, an unsupervised change classification strategy was proposed based on the change direction and magnitude of the dual-pol parameters within the change class. Experimental results exhibited significantly improved detectability across a wide change spectrum compared with previous dual-pol-based change detection approaches. They also demonstrated the possibility of distinguishing different semantic changes without in situ ground data. [ABSTRACT FROM AUTHOR]

Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by application. In this technical note, we present a framework for preparing Sentinel-1 SAR backscatter Analysis-Ready-Data in the Google Earth Engine that combines existing and new Google Earth Engine implementations for additional border noise correction, speckle filtering and radiometric terrain normalization. The proposed framework can be used to generate Sentinel-1 Analysis-Ready-Data suitable for a wide range of land and inland water applications. The Analysis Ready Data preparation framework is implemented in the Google Earth Engine JavaScript and Python APIs. [ABSTRACT FROM AUTHOR]

Acoustic methods are routinely used to provide broad scale information on the geographical distribution of benthic marine habitats and sedimentary environments. Although single-frequency multibeam echosounder surveys have dominated seabed characterisation for decades, multifrequency approaches are now gaining favour in order to capture different frequency responses from the same seabed type. The aim of this study is to develop a robust modelling framework for testing the potential application and value of multifrequency (30, 95, and 300 kHz) multibeam backscatter responses to characterize sediments' grain size in an area with strong geomorphological gradients and benthic ecological variability. We fit a generalized linear model on a multibeam backscatter and its derivatives to examine the explanatory power of single-frequency and multifrequency models with respect to the mean sediment grain size obtained from the grab samples. A strong and statistically significant (p < 0.05) correlation between the mean backscatter and the absolute values of the mean sediment grain size for the data was noted. The root mean squared error (RMSE) values identified the 30 kHz model as the best performing model responsible for explaining the most variation (84.3%) of the mean grain size at a statistically significant output (p < 0.05) with an adjusted r2 = 0.82. Overall, the single low-frequency sources showed a marginal gain on the multifrequency model, with the 30 kHz model driving the significance of this multifrequency model, and the inclusion of the higher frequencies diminished the level of agreement. We recommend further detailed and sufficient ground-truth data to better predict sediment properties and to discriminate benthic habitats to enhance the reliability of multifrequency backscatter data for the monitoring and management of marine protected areas. [ABSTRACT FROM AUTHOR]

Net primary productivity (NPP) is the total amount of organic matter fixed by plants from the atmosphere through photosynthesis and is susceptible to the influences of climate change and human activities. In this study, we employed actual NPP (ANPP), potential NPP (PNPP), and human activity-induced NPP (HNPP) based on the Hurst exponent and statistical analysis to analyze the characteristics of vegetation productivity dynamics and to evaluate the effects of climate and human factors on vegetation productivity in Northeast China (NEC). The increasing trends in ANPP, PNPP, and HNPP accounted for 81.62%, 94.90%, and 89.63% of the total area, respectively, and ANPP in 68.64% of the total area will continue to increase in the future. Climate change played a leading role in vegetation productivity dynamics, which promoted an increase in ANPP in 71.55% of the area, and precipitation was the key climate factor affecting ANPP. The aggravation of human activities, such as increased livestock numbers and intensified agricultural activities, resulted in a decrease in ANPP in the western grasslands, northern Greater Khingan Mountains, and eastern Songnen Plain. In particular, human activities led to a decrease in ANPP in 53.84% of deciduous needleleaf forests. The impact of climate change and human activities varied significantly under different topography, and the percentage of the ANPP increase due to climate change decreased from 71.13% to 53.9% from plains to urgent slopes; however, the percentage of ANPP increase due to human activities increased from 3.44% to 21.74%, and the effect of human activities on the increase of ANPP was more obvious with increasing slope. At different altitudes, the difference in the effect of these two factors was not significant. The results are significant for understanding the factors influencing the vegetation productivity dynamics in NEC and can provide a reference for governments to implement projects to improve the ecosystem. [ABSTRACT FROM AUTHOR]

The advantage of implementing the Water Cloud Model (WCM) is in being able to express complex scattering characteristics in a vegetated area with simple bulk vegetation descriptors. However, there has been a lack of understanding or consensus about the optimal set of vegetation descriptors. In this paper, the original and improved expressions of WCM are evaluated and the optimal vegetation descriptors are presented by examining the relationship between WCM vegetation parameters and the theoretical scattering model predictions. In addition, the condition-specific regression relationship between bulk vegetation descriptors and theoretical scattering and attenuation coefficients, expressed by the A and B parameters in the WCM, is analyzed in relation to the shape, size, and orientation distribution of the scatterer. Furthermore, the influence of radar observation conditions on the parameterization of the WCM is presented. The results show that the particle moisture content and the vegetation water content can be the optimal vegetation descriptors, denoted by the V 1 and V 2 variables in the WCM, respectively. [ABSTRACT FROM AUTHOR]